Measuring the spatial distribution of health rankings in the United States
利用贝叶斯因子分析模型对美国县域人口健康进行排名，发现人口和经济差异解释了大部分排名变化，并指出缺失数据插补带来的不确定性在排名分布中普遍存在。
We rank counties in the United States with respect to population health. We utilize the five observable county health variables used to construct the University of Wisconsin Population Health Institute's County Health Rankings (CHRs). Our method relies on a Bayesian factor analysis model that estimates data-driven weights for our rankings, incorporates county population sizes into the level of rank uncertainty, and allows for spillovers of health stock across county lines. We find that demographic and economic variation explains a large portion of the variation in health rankings. We address the importance of uncertainty caused by imputation of missing data and show that there is a substantial quantity of uncertainty in rankings throughout the rank distribution. Analyzing the health of counties both within and across state lines shows notable degrees of disparity in county health. While we find some disagreement between the ranks of our model and the CHRs, we show that there is additional information gained by utilizing the rankings produced by both methods.